# import glob
# import json
# import logging
# import os
# import re
# import subprocess
# import sys
# import time
# import traceback
# from itertools import chain
# from pathlib import Path
#
# # os.system("wget -P cvec/ https://huggingface.co/spaces/innnky/nanami/resolve/main/checkpoint_best_legacy_500.pt")
# import gradio as gr
# import librosa
# import numpy as np
# import soundfile
# import torch
#
# from compress_model import removeOptimizer
# from edgetts.tts_voices import SUPPORTED_LANGUAGES
# from inference.infer_tool import Svc
# from utils import mix_model
#
# logging.getLogger('numba').setLevel(logging.WARNING)
# logging.getLogger('markdown_it').setLevel(logging.WARNING)
# logging.getLogger('urllib3').setLevel(logging.WARNING)
# logging.getLogger('matplotlib').setLevel(logging.WARNING)
# logging.getLogger('multipart').setLevel(logging.WARNING)
#
# model = None
# spk = None
# debug = False
#
# local_model_root = './trained'
#
# cuda = {}
# if torch.cuda.is_available():
#     for i in range(torch.cuda.device_count()):
#         device_name = torch.cuda.get_device_properties(i).name
#         cuda[f"CUDA:{i} {device_name}"] = f"cuda:{i}"
#
#
# def upload_mix_append_file(files, sfiles):
#     try:
#         if (sfiles is None):
#             file_paths = [file.name for file in files]
#         else:
#             file_paths = [file.name for file in chain(files, sfiles)]
#         p = {file: 100 for file in file_paths}
#         return file_paths, mix_model_output1.update(value=json.dumps(p, indent=2))
#     except Exception as e:
#         if debug:
#             traceback.print_exc()
#         raise gr.Error(e)
#
#
# def mix_submit_click(js, mode):
#     try:
#         assert js.lstrip() != ""
#         modes = {"凸组合": 0, "线性组合": 1}
#         mode = modes[mode]
#         data = json.loads(js)
#         data = list(data.items())
#         model_path, mix_rate = zip(*data)
#         path = mix_model(model_path, mix_rate, mode)
#         return f"成功，文件被保存在了{path}"
#     except Exception as e:
#         if debug:
#             traceback.print_exc()
#         raise gr.Error(e)
#
#
# def updata_mix_info(files):
#     try:
#         if files is None:
#             return mix_model_output1.update(value="")
#         p = {file.name: 100 for file in files}
#         return mix_model_output1.update(value=json.dumps(p, indent=2))
#     except Exception as e:
#         if debug:
#             traceback.print_exc()
#         raise gr.Error(e)
#
#
# def modelAnalysis(model_path, config_path, cluster_model_path, device, enhance, diff_model_path, diff_config_path,
#                   only_diffusion, use_spk_mix, local_model_enabled, local_model_selection):
#     global model
#     try:
#         device = cuda[device] if "CUDA" in device else device
#         cluster_filepath = os.path.split(cluster_model_path.name) if cluster_model_path is not None else "no_cluster"
#         # get model and config path
#         if (local_model_enabled):
#             # local path
#             model_path = glob.glob(os.path.join(local_model_selection, '*.pth'))[0]
#             config_path = glob.glob(os.path.join(local_model_selection, '*.json'))[0]
#         else:
#             # upload from webpage
#             model_path = model_path.name
#             config_path = config_path.name
#         fr = ".pkl" in cluster_filepath[1]
#         model = Svc(model_path,
#                     config_path,
#                     device=device if device != "Auto" else None,
#                     cluster_model_path=cluster_model_path.name if cluster_model_path is not None else "",
#                     nsf_hifigan_enhance=enhance,
#                     diffusion_model_path=diff_model_path.name if diff_model_path is not None else "",
#                     diffusion_config_path=diff_config_path.name if diff_config_path is not None else "",
#                     shallow_diffusion=True if diff_model_path is not None else False,
#                     only_diffusion=only_diffusion,
#                     spk_mix_enable=use_spk_mix,
#                     feature_retrieval=fr
#                     )
#         spks = list(model.spk2id.keys())
#         device_name = torch.cuda.get_device_properties(model.dev).name if "cuda" in str(model.dev) else str(model.dev)
#         msg = f"成功加载模型到设备{device_name}上\n"
#         if cluster_model_path is None:
#             msg += "未加载聚类模型或特征检索模型\n"
#         elif fr:
#             msg += f"特征检索模型{cluster_filepath[1]}加载成功\n"
#         else:
#             msg += f"聚类模型{cluster_filepath[1]}加载成功\n"
#         if diff_model_path is None:
#             msg += "未加载扩散模型\n"
#         else:
#             msg += f"扩散模型{diff_model_path.name}加载成功\n"
#         msg += "当前模型的可用音色：\n"
#         for i in spks:
#             msg += i + " "
#         return sid.update(choices=spks, value=spks[0]), msg
#     except Exception as e:
#         if debug:
#             traceback.print_exc()
#         raise gr.Error(e)
#
#
# def modelUnload():
#     global model
#     if model is None:
#         return sid.update(choices=[], value=""), "没有模型需要卸载!"
#     else:
#         model.unload_model()
#         model = None
#         torch.cuda.empty_cache()
#         return sid.update(choices=[], value=""), "模型卸载完毕!"
#
#
# def vc_infer(output_format, sid, audio_path, truncated_basename, vc_transform, auto_f0, cluster_ratio, slice_db,
#              noise_scale, pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold,
#              k_step, use_spk_mix, second_encoding, loudness_envelope_adjustment):
#     global model
#     _audio = model.slice_inference(
#         audio_path,
#         sid,
#         vc_transform,
#         slice_db,
#         cluster_ratio,
#         auto_f0,
#         noise_scale,
#         pad_seconds,
#         cl_num,
#         lg_num,
#         lgr_num,
#         f0_predictor,
#         enhancer_adaptive_key,
#         cr_threshold,
#         k_step,
#         use_spk_mix,
#         second_encoding,
#         loudness_envelope_adjustment
#     )
#     model.clear_empty()
#     # 构建保存文件的路径，并保存到results文件夹内
#     str(int(time.time()))
#     if not os.path.exists("results"):
#         os.makedirs("results")
#     key = "auto" if auto_f0 else f"{int(vc_transform)}key"
#     cluster = "_" if cluster_ratio == 0 else f"_{cluster_ratio}_"
#     isdiffusion = "sovits"
#     if model.shallow_diffusion:
#         isdiffusion = "sovdiff"
#
#     if model.only_diffusion:
#         isdiffusion = "diff"
#
#     output_file_name = 'result_' + truncated_basename + f'_{sid}_{key}{cluster}{isdiffusion}.{output_format}'
#     output_file = os.path.join("results", output_file_name)
#     soundfile.write(output_file, _audio, model.target_sample, format=output_format)
#     return output_file
#
#
# def vc_fn(sid, input_audio, output_format, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds,
#           cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold, k_step, use_spk_mix,
#           second_encoding, loudness_envelope_adjustment):
#     global model
#     try:
#         if input_audio is None:
#             return "You need to upload an audio", None
#         if model is None:
#             return "You need to upload an model", None
#         if getattr(model, 'cluster_model', None) is None and model.feature_retrieval is False:
#             if cluster_ratio != 0:
#                 return "You need to upload an cluster model or feature retrieval model before assigning cluster ratio!", None
#         # print(input_audio)
#         audio, sampling_rate = soundfile.read(input_audio)
#         # print(audio.shape,sampling_rate)
#         if np.issubdtype(audio.dtype, np.integer):
#             audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
#         # print(audio.dtype)
#         if len(audio.shape) > 1:
#             audio = librosa.to_mono(audio.transpose(1, 0))
#         # 未知原因Gradio上传的filepath会有一个奇怪的固定后缀，这里去掉
#         truncated_basename = Path(input_audio).stem[:-6]
#         processed_audio = os.path.join("raw", f"{truncated_basename}.wav")
#         soundfile.write(processed_audio, audio, sampling_rate, format="wav")
#         output_file = vc_infer(output_format, sid, processed_audio, truncated_basename, vc_transform, auto_f0,
#                                cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num, lg_num, lgr_num, f0_predictor,
#                                enhancer_adaptive_key, cr_threshold, k_step, use_spk_mix, second_encoding,
#                                loudness_envelope_adjustment)
#
#         return "Success", output_file
#     except Exception as e:
#         if debug:
#             traceback.print_exc()
#         raise gr.Error(e)
#
#
# def text_clear(text):
#     return re.sub(r"[\n\,\(\) ]", "", text)
#
#
# def vc_fn2(_text, _lang, _gender, _rate, _volume, sid, output_format, vc_transform, auto_f0, cluster_ratio, slice_db,
#            noise_scale, pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold, k_step,
#            use_spk_mix, second_encoding, loudness_envelope_adjustment):
#     global model
#     try:
#         if model is None:
#             return "You need to upload an model", None
#         if getattr(model, 'cluster_model', None) is None and model.feature_retrieval is False:
#             if cluster_ratio != 0:
#                 return "You need to upload an cluster model or feature retrieval model before assigning cluster ratio!", None
#         _rate = f"+{int(_rate * 100)}%" if _rate >= 0 else f"{int(_rate * 100)}%"
#         _volume = f"+{int(_volume * 100)}%" if _volume >= 0 else f"{int(_volume * 100)}%"
#         if _lang == "Auto":
#             _gender = "Male" if _gender == "男" else "Female"
#             subprocess.run([sys.executable, "edgetts/tts.py", _text, _lang, _rate, _volume, _gender])
#         else:
#             subprocess.run([sys.executable, "edgetts/tts.py", _text, _lang, _rate, _volume])
#         target_sr = 44100
#         y, sr = librosa.load("tts.wav")
#         resampled_y = librosa.resample(y, orig_sr=sr, target_sr=target_sr)
#         soundfile.write("tts.wav", resampled_y, target_sr, subtype="PCM_16")
#         input_audio = "tts.wav"
#         # audio, _ = soundfile.read(input_audio)
#         output_file_path = vc_infer(output_format, sid, input_audio, "tts", vc_transform, auto_f0, cluster_ratio,
#                                     slice_db, noise_scale, pad_seconds, cl_num, lg_num, lgr_num, f0_predictor,
#                                     enhancer_adaptive_key, cr_threshold, k_step, use_spk_mix, second_encoding,
#                                     loudness_envelope_adjustment)
#         os.remove("tts.wav")
#         return "Success", output_file_path
#     except Exception as e:
#         if debug: traceback.print_exc()  # noqa: E701
#         raise gr.Error(e)
#
#
# def model_compression(_model):
#     if _model == "":
#         return "请先选择要压缩的模型"
#     else:
#         model_path = os.path.split(_model.name)
#         filename, extension = os.path.splitext(model_path[1])
#         output_model_name = f"{filename}_compressed{extension}"
#         output_path = os.path.join(os.getcwd(), output_model_name)
#         removeOptimizer(_model.name, output_path)
#         return f"模型已成功被保存在了{output_path}"
#
#
# def scan_local_models():
#     res = []
#     candidates = glob.glob(os.path.join(local_model_root, '**', '*.json'), recursive=True)
#     candidates = set([os.path.dirname(c) for c in candidates])
#     for candidate in candidates:
#         jsons = glob.glob(os.path.join(candidate, '*.json'))
#         pths = glob.glob(os.path.join(candidate, '*.pth'))
#         if (len(jsons) == 1 and len(pths) == 1):
#             # must contain exactly one json and one pth file
#             res.append(candidate)
#     return res
#
#
# def local_model_refresh_fn():
#     choices = scan_local_models()
#     return gr.Dropdown.update(choices=choices)
#
#
# def debug_change():
#     global debug
#     debug = debug_button.value
#
#
# with gr.Blocks(
#         theme=gr.themes.Base(
#             primary_hue=gr.themes.colors.green,
#             font=["Source Sans Pro", "Arial", "sans-serif"],
#             font_mono=['JetBrains mono', "Consolas", 'Courier New']
#         ),
# ) as app:
#     with gr.Tabs():
#         with gr.TabItem("推理"):
#             gr.Markdown(value="""
#                 So-vits-svc 4.0 推理 webui
#                 """)
#             with gr.Row(variant="panel"):
#                 with gr.Column():
#                     gr.Markdown(value="""
#                         <font size=2> 模型设置</font>
#                         """)
#                     with gr.Tabs():
#                         # invisible checkbox that tracks tab status
#                         local_model_enabled = gr.Checkbox(value=False, visible=False)
#                         with gr.TabItem('上传') as local_model_tab_upload:
#                             with gr.Row():
#                                 model_path = gr.File(label="选择模型文件")
#                                 config_path = gr.File(label="选择配置文件")
#                         with gr.TabItem('本地') as local_model_tab_local:
#                             gr.Markdown(f'模型应当放置于{local_model_root}文件夹下')
#                             local_model_refresh_btn = gr.Button('刷新本地模型列表')
#                             local_model_selection = gr.Dropdown(label='选择模型文件夹', choices=[], interactive=True)
#                     with gr.Row():
#                         diff_model_path = gr.File(label="选择扩散模型文件")
#                         diff_config_path = gr.File(label="选择扩散模型配置文件")
#                     cluster_model_path = gr.File(label="选择聚类模型或特征检索文件（没有可以不选）")
#                     device = gr.Dropdown(label="推理设备，默认为自动选择CPU和GPU", choices=["Auto", *cuda.keys(), "cpu"],
#                                          value="Auto")
#                     enhance = gr.Checkbox(
#                         label="是否使用NSF_HIFIGAN增强,该选项对部分训练集少的模型有一定的音质增强效果，但是对训练好的模型有反面效果，默认关闭",
#                         value=False)
#                     only_diffusion = gr.Checkbox(
#                         label="是否使用全扩散推理，开启后将不使用So-VITS模型，仅使用扩散模型进行完整扩散推理，默认关闭",
#                         value=False)
#                 with gr.Column():
#                     gr.Markdown(value="""
#                         <font size=3>左侧文件全部选择完毕后(全部文件模块显示download)，点击“加载模型”进行解析：</font>
#                         """)
#                     model_load_button = gr.Button(value="加载模型", variant="primary")
#                     model_unload_button = gr.Button(value="卸载模型", variant="primary")
#                     sid = gr.Dropdown(label="音色（说话人）")
#                     sid_output = gr.Textbox(label="Output Message")
#
#             with gr.Row(variant="panel"):
#                 with gr.Column():
#                     gr.Markdown(value="""
#                         <font size=2> 推理设置</font>
#                         """)
#                     auto_f0 = gr.Checkbox(
#                         label="自动f0预测，配合聚类模型f0预测效果更好,会导致变调功能失效（仅限转换语音，歌声勾选此项会究极跑调）",
#                         value=False)
#                     f0_predictor = gr.Dropdown(
#                         label="选择F0预测器,可选择crepe,pm,dio,harvest,rmvpe,默认为pm(注意：crepe为原F0使用均值滤波器)",
#                         choices=["pm", "dio", "harvest", "crepe", "rmvpe"], value="pm")
#                     vc_transform = gr.Number(label="变调（整数，可以正负，半音数量，升高八度就是12）", value=0)
#                     cluster_ratio = gr.Number(
#                         label="聚类模型/特征检索混合比例，0-1之间，0即不启用聚类/特征检索。使用聚类/特征检索能提升音色相似度，但会导致咬字下降（如果使用建议0.5左右）",
#                         value=0)
#                     slice_db = gr.Number(label="切片阈值", value=-40)
#                     output_format = gr.Radio(label="音频输出格式", choices=["wav", "flac", "mp3"], value="wav")
#                     noise_scale = gr.Number(label="noise_scale 建议不要动，会影响音质，玄学参数", value=0.4)
#                     k_step = gr.Slider(label="浅扩散步数，只有使用了扩散模型才有效，步数越大越接近扩散模型的结果",
#                                        value=100, minimum=1, maximum=1000)
#                 with gr.Column():
#                     pad_seconds = gr.Number(
#                         label="推理音频pad秒数，由于未知原因开头结尾会有异响，pad一小段静音段后就不会出现", value=0.5)
#                     cl_num = gr.Number(label="音频自动切片，0为不切片，单位为秒(s)", value=0)
#                     lg_num = gr.Number(
#                         label="两端音频切片的交叉淡入长度，如果自动切片后出现人声不连贯可调整该数值，如果连贯建议采用默认值0，注意，该设置会影响推理速度，单位为秒/s",
#                         value=0)
#                     lgr_num = gr.Number(
#                         label="自动音频切片后，需要舍弃每段切片的头尾。该参数设置交叉长度保留的比例，范围0-1,左开右闭",
#                         value=0.75)
#                     enhancer_adaptive_key = gr.Number(label="使增强器适应更高的音域(单位为半音数)|默认为0", value=0)
#                     cr_threshold = gr.Number(
#                         label="F0过滤阈值，只有启动crepe时有效. 数值范围从0-1. 降低该值可减少跑调概率，但会增加哑音",
#                         value=0.05)
#                     loudness_envelope_adjustment = gr.Number(
#                         label="输入源响度包络替换输出响度包络融合比例，越靠近1越使用输出响度包络", value=0)
#                     second_encoding = gr.Checkbox(
#                         label="二次编码，浅扩散前会对原始音频进行二次编码，玄学选项，效果时好时差，默认关闭", value=False)
#                     use_spk_mix = gr.Checkbox(label="动态声线融合", value=False, interactive=False)
#             with gr.Tabs():
#                 with gr.TabItem("音频转音频"):
#                     vc_input3 = gr.Audio(label="选择音频", type="filepath")
#                     vc_submit = gr.Button("音频转换", variant="primary")
#                 with gr.TabItem("文字转音频"):
#                     text2tts = gr.Textbox(label="在此输入要转译的文字。注意，使用该功能建议打开F0预测，不然会很怪")
#                     with gr.Row():
#                         tts_gender = gr.Radio(label="说话人性别", choices=["男", "女"], value="男")
#                         tts_lang = gr.Dropdown(label="选择语言，Auto为根据输入文字自动识别", choices=SUPPORTED_LANGUAGES,
#                                                value="Auto")
#                         tts_rate = gr.Slider(label="TTS语音变速（倍速相对值）", minimum=-1, maximum=3, value=0, step=0.1)
#                         tts_volume = gr.Slider(label="TTS语音音量（相对值）", minimum=-1, maximum=1.5, value=0, step=0.1)
#                     vc_submit2 = gr.Button("文字转换", variant="primary")
#             with gr.Row():
#                 with gr.Column():
#                     vc_output1 = gr.Textbox(label="Output Message")
#                 with gr.Column():
#                     vc_output2 = gr.Audio(label="Output Audio", interactive=False)
#
#         with gr.TabItem("小工具/实验室特性"):
#             gr.Markdown(value="""
#                         <font size=2> So-vits-svc 4.0 小工具/实验室特性</font>
#                         """)
#             with gr.Tabs():
#                 with gr.TabItem("静态声线融合"):
#                     gr.Markdown(value="""
#                         <font size=2> 介绍:该功能可以将多个声音模型合成为一个声音模型(多个模型参数的凸组合或线性组合)，从而制造出现实中不存在的声线
#                                           注意：
#                                           1.该功能仅支持单说话人的模型
#                                           2.如果强行使用多说话人模型，需要保证多个模型的说话人数量相同，这样可以混合同一个SpaekerID下的声音
#                                           3.保证所有待混合模型的config.json中的model字段是相同的
#                                           4.输出的混合模型可以使用待合成模型的任意一个config.json，但聚类模型将不能使用
#                                           5.批量上传模型的时候最好把模型放到一个文件夹选中后一起上传
#                                           6.混合比例调整建议大小在0-100之间，也可以调为其他数字，但在线性组合模式下会出现未知的效果
#                                           7.混合完毕后，文件将会保存在项目根目录中，文件名为output.pth
#                                           8.凸组合模式会将混合比例执行Softmax使混合比例相加为1，而线性组合模式不会
#                         </font>
#                         """)
#                     mix_model_path = gr.Files(label="选择需要混合模型文件")
#                     mix_model_upload_button = gr.UploadButton("选择/追加需要混合模型文件", file_count="multiple")
#                     mix_model_output1 = gr.Textbox(
#                         label="混合比例调整，单位/%",
#                         interactive=True
#                     )
#                     mix_mode = gr.Radio(choices=["凸组合", "线性组合"], label="融合模式", value="凸组合",
#                                         interactive=True)
#                     mix_submit = gr.Button("声线融合启动", variant="primary")
#                     mix_model_output2 = gr.Textbox(
#                         label="Output Message"
#                     )
#                     mix_model_path.change(updata_mix_info, [mix_model_path], [mix_model_output1])
#                     mix_model_upload_button.upload(upload_mix_append_file, [mix_model_upload_button, mix_model_path],
#                                                    [mix_model_path, mix_model_output1])
#                     mix_submit.click(mix_submit_click, [mix_model_output1, mix_mode], [mix_model_output2])
#
#                 with gr.TabItem("模型压缩工具"):
#                     gr.Markdown(value="""
#                         该工具可以实现对模型的体积压缩，在**不影响模型推理功能**的情况下，将原本约600M的So-VITS模型压缩至约200M, 大大减少了硬盘的压力。
#                         **注意：压缩后的模型将无法继续训练，请在确认封炉后再压缩。**
#                     """)
#                     model_to_compress = gr.File(label="模型上传")
#                     compress_model_btn = gr.Button("压缩模型", variant="primary")
#                     compress_model_output = gr.Textbox(label="输出信息", value="")
#
#                     compress_model_btn.click(model_compression, [model_to_compress], [compress_model_output])
#
#     with gr.Tabs():
#         with gr.Row(variant="panel"):
#             with gr.Column():
#                 gr.Markdown(value="""
#                     <font size=2> WebUI设置</font>
#                     """)
#                 debug_button = gr.Checkbox(label="Debug模式，如果向社区反馈BUG需要打开，打开后控制台可以显示具体错误提示",
#                                            value=debug)
#         # refresh local model list
#         local_model_refresh_btn.click(local_model_refresh_fn, outputs=local_model_selection)
#         # set local enabled/disabled on tab switch
#         local_model_tab_upload.select(lambda: False, outputs=local_model_enabled)
#         local_model_tab_local.select(lambda: True, outputs=local_model_enabled)
#
#         vc_submit.click(vc_fn,
#                         [sid, vc_input3, output_format, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale,
#                          pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold,
#                          k_step, use_spk_mix, second_encoding, loudness_envelope_adjustment], [vc_output1, vc_output2])
#         vc_submit2.click(vc_fn2,
#                          [text2tts, tts_lang, tts_gender, tts_rate, tts_volume, sid, output_format, vc_transform,
#                           auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num, lg_num, lgr_num,
#                           f0_predictor, enhancer_adaptive_key, cr_threshold, k_step, use_spk_mix, second_encoding,
#                           loudness_envelope_adjustment], [vc_output1, vc_output2])
#
#         debug_button.change(debug_change, [], [])
#         model_load_button.click(modelAnalysis,
#                                 [model_path, config_path, cluster_model_path, device, enhance, diff_model_path,
#                                  diff_config_path, only_diffusion, use_spk_mix, local_model_enabled,
#                                  local_model_selection], [sid, sid_output])
#         model_unload_button.click(modelUnload, [], [sid, sid_output])
#     os.system("start http://127.0.0.1:7860")
#     app.launch()
#
#
#
